
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import folium
import glob
%matplotlib inline


files = glob.glob("data/*.csv")
frames=[]
for file in files:
    frames.append(pd.read_csv(file, index_col=None, header=0))

/home/abdulrahman/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (14) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
/home/abdulrahman/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (13,14) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
/home/abdulrahman/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (13) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,


df = pd.concat(frames, axis=0, ignore_index=True)


# save new dataset
df.to_csv('bike.csv', index=False)


df = pd.read_csv('bike.csv')


df.head()


df.sample(5)


# Info about the dataset
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2506983 entries, 0 to 2506982
Data columns (total 15 columns):
 #   Column                   Dtype  
---  ------                   -----  
 0   duration_sec             int64  
 1   start_time               object 
 2   end_time                 object 
 3   start_station_id         float64
 4   start_station_name       object 
 5   start_station_latitude   float64
 6   start_station_longitude  float64
 7   end_station_id           float64
 8   end_station_name         object 
 9   end_station_latitude     float64
 10  end_station_longitude    float64
 11  bike_id                  int64  
 12  user_type                object 
 13  bike_share_for_all_trip  object 
 14  rental_access_method     object 
dtypes: float64(6), int64(2), object(7)
memory usage: 286.9+ MB


# more information about the dataset
df.describe().transpose()


# check Null/Nan Values
missing_values = df.isna().sum()
df.isna().sum()

duration_sec                     0
start_time                       0
end_time                         0
start_station_id             80734
start_station_name           80133
start_station_latitude           0
start_station_longitude          0
end_station_id               82902
end_station_name             82350
end_station_latitude             0
end_station_longitude            0
bike_id                          0
user_type                        0
bike_share_for_all_trip     243259
rental_access_method       2386145
dtype: int64


# Check for duplicates
df[df.duplicated()==True]


df.drop('rental_access_method', axis=1, inplace=True)
df.sample(5)


# missing values is small percentage of total data, so I will drop it
df.dropna(inplace=True)


df.isna().sum()

duration_sec               0
start_time                 0
end_time                   0
start_station_id           0
start_station_name         0
start_station_latitude     0
start_station_longitude    0
end_station_id             0
end_station_name           0
end_station_latitude       0
end_station_longitude      0
bike_id                    0
user_type                  0
bike_share_for_all_trip    0
dtype: int64


df.sample(5)


df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2262979 entries, 0 to 2355470
Data columns (total 14 columns):
 #   Column                   Dtype  
---  ------                   -----  
 0   duration_sec             int64  
 1   start_time               object 
 2   end_time                 object 
 3   start_station_id         float64
 4   start_station_name       object 
 5   start_station_latitude   float64
 6   start_station_longitude  float64
 7   end_station_id           float64
 8   end_station_name         object 
 9   end_station_latitude     float64
 10  end_station_longitude    float64
 11  bike_id                  int64  
 12  user_type                object 
 13  bike_share_for_all_trip  object 
dtypes: float64(6), int64(2), object(6)
memory usage: 259.0+ MB


# change to datetime format
df.start_time = pd.to_datetime(df.start_time)
df.end_time = pd.to_datetime(df.end_time)


# insert new columns
df.insert(0, 'duration_mins', (df.duration_sec/60))
df.insert(1, 'start_day', (df.start_time.dt.strftime('%a')))
df.insert(2, 'end_day', (df.end_time.dt.strftime('%a')))
df.insert(3, 'start_month', (df.start_time.dt.strftime('%b')))
df.insert(4, 'end_month', (df.end_time.dt.strftime('%b')))
df.insert(5, 'start_hour', (df.start_time.dt.hour))
df.insert(6, 'end_hour', (df.end_time.dt.hour))


df.head()


# calculate the distance in miles between start and end stations
df['distance_miles'] = 3958.756 * 2 * np.arcsin(np.sqrt(np.sin((np.radians(df['end_station_latitude']) - \
                    np.radians(df['start_station_latitude']))/2)**2 + np.cos(np.radians(df['start_station_latitude'])) * \
                    np.cos(np.radians(df['end_station_latitude'])) * np.sin((np.radians(df['end_station_longitude']) - \
                    np.radians(df['start_station_longitude']))/2)**2))


df.sample(5)


df.bike_id = df.bike_id.astype(str)
df.start_station_id = df.start_station_id.astype(str)
df.end_station_id = df.end_station_id.astype(str)
df.duration_mins = df.duration_mins.round()
df.duration_mins = df.duration_mins.astype(np.int64)
df.user_type = df.user_type.astype('category')


df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2262979 entries, 0 to 2355470
Data columns (total 22 columns):
 #   Column                   Dtype         
---  ------                   -----         
 0   duration_mins            int64         
 1   start_day                object        
 2   end_day                  object        
 3   start_month              object        
 4   end_month                object        
 5   start_hour               int64         
 6   end_hour                 int64         
 7   duration_sec             int64         
 8   start_time               datetime64[ns]
 9   end_time                 datetime64[ns]
 10  start_station_id         object        
 11  start_station_name       object        
 12  start_station_latitude   float64       
 13  start_station_longitude  float64       
 14  end_station_id           object        
 15  end_station_name         object        
 16  end_station_latitude     float64       
 17  end_station_longitude    float64       
 18  bike_id                  object        
 19  user_type                category      
 20  bike_share_for_all_trip  object        
 21  distance_miles           float64       
dtypes: category(1), datetime64[ns](2), float64(5), int64(4), object(10)
memory usage: 382.0+ MB


df.drop('start_time', axis=1, inplace=True)
df.drop('end_time', axis=1, inplace=True)
df.drop('duration_sec', axis=1, inplace=True)


df.sample(5)


sns.set_style('darkgrid')


fig, ax = plt.subplots(nrows=2, figsize = [12, 6])
ax[0].hist(data = df, x='duration_mins', bins=np.arange(0, df.duration_mins.mean()+40, 1), color='b', alpha=0.8, edgecolor='k')
ax[1].hist(data = df, x = 'duration_mins', bins=10 ** np.arange(0, 2.0, 0.09), color='b', alpha=0.8, edgecolor='k')
plt.xscale('log')
plt.sca(ax[0])
plt.xticks(np.arange(0, 55, 1))
plt.title("Rental duration in minutes - Normal Scale", fontsize=16)
plt.ylim(0, 180000)
plt.xlabel("Rental duration in minutes", fontsize=16)
plt.sca(ax[1])
plt.title("Rental duration in minutes - Log Scale", fontsize=16)
plt.xlabel("Rental duration in minutes - Log x scale", fontsize=16)
plt.tight_layout()
plt.show()


sns.countplot(data = df, x = 'user_type', color='b')
sns.set(rc={'figure.figsize':(12, 6)})
plt.title("Number of Subscribers vs Customers", fontsize=16)
plt.xlabel("User Type", fontsize=16)
plt.show()


sub_count = df.user_type.value_counts()[0]
cust_count = df.user_type.value_counts()[1]
total = sub_count+cust_count
plt.pie([sub_count/total*100, cust_count/total*100], autopct='%1.1f%%', labels = ['Subscribers', 'Customers'], startangle=90)
plt.title('Percentage of Subscribers vs Customers', fontsize=16)
plt.axis('equal')
plt.show()


days, months = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], df.start_month.unique()


# Distribution of rentals across days of the week, months and hour
fig, ax = plt.subplots(nrows=3, figsize = [14, 8])
sns.countplot(data = df, x = 'start_hour', color = 'b', alpha=0.8, ax = ax[0])
plt.sca(ax[0])
plt.xlabel("Start Hour", fontsize=16)
sns.countplot(data = df, x = 'start_day', color = 'b', alpha=0.8, ax = ax[1], order=days)
plt.sca(ax[1])
plt.xlabel("Start Day", fontsize=16)
sns.countplot(data = df, x = 'start_month', color = 'b', alpha=0.8, ax = ax[2])
plt.sca(ax[2])
plt.xlabel("Start Month", fontsize=16)
plt.tight_layout()
plt.show()


sns.countplot(df[df.user_type == 'Subscriber'].bike_share_for_all_trip)
plt.title("Subscribers in Bike Share for All scheme", fontsize=16)
plt.show()

/home/abdulrahman/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


no_count = df.bike_share_for_all_trip.value_counts()[0]
yes_count = df.bike_share_for_all_trip.value_counts()[1]
plt.pie([no_count/(no_count+yes_count)*100, yes_count/(no_count+yes_count)*100], autopct='%1.1f%%', labels=['No', 'Yes'], startangle=90)
plt.title("Percentage of Subscribers in Bike Share for All scheme", fontsize=15)
plt.axis('equal')
plt.show()


sub_dist = df[df.user_type == 'Subscriber'].distance_miles.sum()
cust_dist = df[df.user_type == 'Customer'].distance_miles.sum()
tot_dist = df.distance_miles.sum()


plt.pie([sub_dist/tot_dist*100, cust_dist/tot_dist*100], autopct='%1.1f%%', labels=['Subscribers', 'Customers'], startangle=90)
plt.title("Distance travelled", fontsize=16)
plt.axis('equal')
plt.show()


sub_dur = df[df.user_type == 'Subscriber'].duration_mins.sum()
cust_dur = df[df.user_type == 'Customer'].duration_mins.sum()
tot_dur = df.distance_miles.sum()


plt.pie([sub_dur/tot_dur*100, cust_dur/tot_dur*100], autopct='%1.1f%%', labels=['Subscribers', 'Customers'], startangle=90)
plt.axis('equal')
plt.title('Rental duration of Subscribers and Customers', fontsize=15)
plt.show()


fig, ax = plt.subplots(nrows=2, figsize = [12, 5])
sub = df[df.user_type=='Subscriber']
ax[0].hist(sub.duration_mins, bins=np.arange(0, 40, 1), color='b', alpha=0.8, edgecolor='k')
ax[1].hist(sub.duration_mins, bins=10 ** np.arange(0, 2, 0.08), color='b', alpha=0.8, edgecolor='k')
plt.xscale('log')
plt.sca(ax[0])
plt.xticks(np.arange(0, 40, 1))
plt.title("Rental duration for subscribers in minutes - Normal Scale", fontsize=16)
plt.xlabel("Rental duration for subscribers in minutes", fontsize=16)
plt.sca(ax[1])
plt.title("Rental duration for subscribers in minutes - Log Scale", fontsize=16)
plt.xlabel("Rental duration for subscribers in minutes", fontsize=16)
plt.tight_layout()
plt.show()


fig, ax = plt.subplots(nrows=2, figsize = [12, 5])
sub = df[df.user_type=='Customer']
ax[0].hist(sub.duration_mins, bins=np.arange(0, 40, 1), color='b', alpha=0.8, edgecolor='k')
ax[1].hist(sub.duration_mins, bins=10 ** np.arange(0, 2, 0.08), color='b', alpha=0.8, edgecolor='k')
plt.xscale('log')
plt.sca(ax[0])
plt.xticks(np.arange(0, 40, 1))
plt.yticks(np.arange(0, 18000, 4000))
plt.title("Rental duration for customers in minutes - Normal Scale", fontsize=16)
plt.xlabel("Rental duration for customers in minutes", fontsize=16)
plt.sca(ax[1])
plt.title("Rental duration for customers in minutes - Log Scale", fontsize=16)
plt.xlabel("Rental duration for customers in minutes", fontsize=16)
plt.tight_layout()
plt.show()


fig, ax = plt.subplots(nrows=2, figsize = [12, 5])
sub = df[df.bike_share_for_all_trip=='Yes']
ax[0].hist(sub.duration_mins, bins=np.arange(0, 40, 1), color='b', alpha=0.8, edgecolor='k')
plt.sca(ax[0])
plt.xticks(np.arange(0, 40, 1))
plt.title("Rental duration for all users in minutes - Normal Scale", fontsize=16)
plt.xlabel("Rental duration for all users in minutes", fontsize=16)
ax[1].hist(sub.duration_mins, bins=10 ** np.arange(0, 2, 0.08), color='b', alpha=0.8, edgecolor='k')
plt.sca(ax[1])
plt.xscale('log')
plt.title("Rental duration for all users in minutes - Log Scale", fontsize=16)
plt.xlabel("Rental duration for all users in minutes", fontsize=16)
plt.tight_layout()
plt.show()


fig, ax = plt.subplots(nrows=3, figsize = [20, 15])
sub = df[df.user_type=='Subscriber']
bins = np.arange(0, 3.5, 0.05)
ax[0].hist(sub.distance_miles, bins=bins, color='b', alpha=0.8, edgecolor='k')
plt.sca(ax[0])
plt.xticks(np.arange(0, 4, 0.1))
plt.title("Subscribers", fontsize=16)
plt.xlabel("Distance in miles", fontsize=16)
cust = df[df.user_type=='Customer']
ax[1].hist(cust.distance_miles, bins=bins,  color='b', alpha=0.8, edgecolor='k')
plt.sca(ax[1])
plt.xticks(np.arange(0, 4, 0.1))
plt.title("Customers", fontsize=16)
plt.xlabel("Distance in miles", fontsize=16)
ax[2].hist(df[df.bike_share_for_all_trip == 'Yes'].distance_miles, bins=bins,  color='b', alpha=0.8, edgecolor='k')
plt.sca(ax[2])
plt.xticks(np.arange(0, 4, 0.1))
plt.title("Bike Share", fontsize=16)
plt.xlabel("Distance in miles", fontsize=16)
plt.tight_layout()
plt.show()


data = df[df.duration_mins < 20]
sns.catplot(data=data, y='duration_mins', col="user_type", kind='box')
sns.catplot(data=data, y='duration_mins', col="user_type", kind='violin')
plt.suptitle("Duration in minutes of Rentals for Subscribers and Customers", y=1.1)
plt.show()


a = df[df.user_type == 'Customer'].start_station_name.value_counts()[:40]
sns.barplot(a, a.index, palette='autumn')
sns.set(rc={'figure.figsize':(16, 10)})
plt.yticks(fontsize=16)
plt.tight_layout()
plt.show()

/home/abdulrahman/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


a = df[df.user_type == 'Subscriber'].start_station_name.value_counts()[:40]
sns.barplot(a, a.index, palette='autumn')
sns.set(rc={'figure.figsize':(16, 10)})
plt.yticks(fontsize=16)
plt.tight_layout()
plt.show()

/home/abdulrahman/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


a = df[df.bike_share_for_all_trip == "Yes"].start_station_name.value_counts()[:40]
sns.barplot(a, a.index, palette='autumn')
sns.set(rc={'figure.figsize':(16, 10)})
plt.yticks(fontsize=16)
plt.tight_layout()
plt.show()

/home/abdulrahman/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


a = sns.FacetGrid(df[df.user_type=='Customer'], col='start_day', col_wrap=5, col_order=days, sharey=True)
a = (a.map(plt.hist, 'start_hour', bins=np.arange(0, 24, 1), color='b', alpha=0.8, edgecolor='k').set_axis_labels("Day of the week", "Start Hour"))
a.set(xticks=np.arange(0, 24, 2))
plt.suptitle("Start Hour vs Day of the Week for Subscribers", y=1.1)
plt.show()


a = df[df.user_type=='Subscriber'].groupby(['start_hour', 'start_day'])['bike_id'].size().reset_index().pivot('start_hour', 'start_day', 'bike_id')[days];
heat_map = sns.heatmap(a, cmap = 'viridis_r', annot=True, robust=True, fmt='d', linewidths=.5)
plt.title('Subscriber', y=1)
plt.xlabel('Weekday', labelpad = 18)
plt.ylabel('Start Time Hour',  labelpad = 18)
plt.show()


df2 = df[['start_station_name', 'start_station_latitude', 'start_station_longitude', 'end_station_name', 
             'end_station_latitude', 'end_station_longitude', 'user_type', 'bike_share_for_all_trip']]


df2.start_station_latitude, df2.start_station_longitude = df2.start_station_latitude.astype(str), df2.start_station_longitude.astype(str)
df2.end_station_latitude, df2.end_station_longitude = df2.end_station_latitude.astype(str), df2.end_station_longitude.astype(str)

/home/abdulrahman/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py:5168: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self[name] = value


from IPython.display import Image


fm = folium.Map(location=[37.8272, -122.2913], tiles='Stamen Terrain', zoom_start=10)
c = df2[df2.user_type == 'Customer'].start_station_latitude.value_counts()[:40].index
test = df2[df2.user_type == 'Customer']
d = test.start_station_longitude.value_counts()[:40].index


for a, b in zip(c, d):
    pop = df2[df2.start_station_latitude==a].start_station_name.unique()[0]
    tooltip = 'Click me!'
    icon=folium.Icon(color='black', icon='bicycle', icon_color="red", prefix='fa', icon_size=(0, 0))
    folium.Marker(location = [float(a), float(b)], popup=pop, tooltip='click here', icon=icon).add_to(fm)
map_cust_file = 'map_customers.html'
map_cust_img = 'map_customers.png'
fm.save(map_cust_file)


Image(filename=map_cust_img)


fm = folium.Map(location=[37.8272, -122.2913], tiles='Stamen Terrain', zoom_start=10)
c = df2[df2.user_type == 'Subscriber'].start_station_latitude.value_counts()[:40].index
test = df2[df2.user_type == 'Subscriber']
d = test.start_station_longitude.value_counts()[:40].index


for a, b in zip(c, d):
    pop = df2[df2.start_station_latitude==a].start_station_name.unique()[0]
    icon=folium.Icon(color='black', icon='bicycle', icon_color="red", prefix='fa', icon_size=(0, 0))
    folium.Marker(location = [float(a), float(b)], popup=pop, tooltip='click here', icon=icon).add_to(fm)
map_subs_file = 'map_subscribers.html'
map_subs_img = 'map_subscribers.png'
fm.save(map_cust_file)


Image(filename=map_subs_img)


fm = folium.Map(location=[37.8272, -122.2913], tiles='Stamen Terrain', zoom_start=10)
c = df2[df2.bike_share_for_all_trip=='Yes'].start_station_latitude.value_counts()[:40].index
d = df2[df2.bike_share_for_all_trip=='Yes'].start_station_longitude.value_counts()[:40].index
for a, b in zip(c, d):
    pop = df2[df2.start_station_latitude==a].start_station_name.unique()[0]
    icon=folium.Icon(color='black', icon='bicycle', icon_color="red", prefix='fa', icon_size=(0, 0))
    folium.Marker(location = [float(a), float(b)], popup=pop, tooltip='click here', icon=icon).add_to(fm)

map_bikeshare_file = 'map_bikeshare.html'
map_bikeshare_img = 'map_bikeshare.png'
fm.save(map_cust_file)


Image(filename=map_bikeshare_img)

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	bike_share_for_all_trip	rental_access_method
0	80825	2019-01-31 17:57:44.6130	2019-02-01 16:24:49.8640	229.0	Foothill Blvd at 42nd Ave	37.775745	-122.213037	196.0	Grand Ave at Perkins St	37.808894	-122.256460	4861	Subscriber	No	NaN
1	65900	2019-01-31 20:58:33.8860	2019-02-01 15:16:54.1730	4.0	Cyril Magnin St at Ellis St	37.785881	-122.408915	134.0	Valencia St at 24th St	37.752428	-122.420628	5506	Subscriber	No	NaN
2	62633	2019-01-31 18:06:52.9240	2019-02-01 11:30:46.5300	245.0	Downtown Berkeley BART	37.870139	-122.268422	157.0	65th St at Hollis St	37.846784	-122.291376	2717	Customer	No	NaN
3	44680	2019-01-31 19:46:09.7190	2019-02-01 08:10:50.3180	85.0	Church St at Duboce Ave	37.770083	-122.429156	53.0	Grove St at Divisadero	37.775946	-122.437777	4557	Customer	No	NaN
4	60709	2019-01-31 14:19:01.5410	2019-02-01 07:10:51.0650	16.0	Steuart St at Market St	37.794130	-122.394430	28.0	The Embarcadero at Bryant St	37.787168	-122.388098	2100	Customer	No	NaN

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	bike_share_for_all_trip	rental_access_method
1065715	996	2019-06-28 10:30:03.0400	2019-06-28 10:46:39.5470	114.0	Rhode Island St at 17th St	37.764478	-122.402570	24.0	Spear St at Folsom St	37.789677	-122.390428	920	Subscriber	No	NaN
27466	800	2019-01-29 07:44:41.9210	2019-01-29 07:58:02.4310	16.0	Steuart St at Market St	37.794130	-122.394430	104.0	4th St at 16th St	37.767045	-122.390833	5306	Subscriber	No	NaN
1939627	3344	2019-10-30 22:10:10.4370	2019-10-30 23:05:54.5480	460.0	Terry Francois Blvd at Warriors Way	37.769095	-122.386333	11.0	Davis St at Jackson St	37.797280	-122.398436	12474	Customer	No	NaN
52532	829	2019-01-25 14:47:30.4260	2019-01-25 15:01:20.0570	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	368.0	Myrtle St at Polk St	37.785434	-122.419622	5242	Subscriber	No	NaN
1151804	435	2019-06-14 17:33:40.8670	2019-06-14 17:40:56.1400	50.0	2nd St at Townsend St	37.780526	-122.390288	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	1088	Subscriber	No	NaN

	count	mean	std	min	25%	50%	75%	max
duration_sec	2506983.0	807.648330	1974.713981	60.000000	359.000000	571.000000	887.000000	912110.00
start_station_id	2426249.0	146.504749	122.317102	3.000000	47.000000	105.000000	243.000000	498.00
start_station_latitude	2506983.0	37.765064	0.136370	0.000000	37.769305	37.780526	37.795393	45.51
start_station_longitude	2506983.0	-122.349919	0.308965	-122.514299	-122.413004	-122.398285	-122.291415	0.00
end_station_id	2424081.0	142.704424	121.429649	3.000000	43.000000	101.000000	239.000000	498.00
end_station_latitude	2506983.0	37.764219	0.239289	0.000000	37.770030	37.780760	37.795873	45.51
end_station_longitude	2506983.0	-122.345908	0.708042	-122.514287	-122.411726	-122.398113	-122.293400	0.00
bike_id	2506983.0	27898.327162	114606.651187	4.000000	1952.000000	4420.000000	9682.000000	999941.00

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	bike_share_for_all_trip
1953118	727	2019-10-29 12:40:14.1720	2019-10-29 12:52:22.1050	389.0	Taylor St at 9th St	37.353062	-121.891937	308.0	San Pedro Square	37.336802	-121.894090	1256	Subscriber	No
610157	541	2019-03-04 17:13:13.3600	2019-03-04 17:22:15.0490	5.0	Powell St BART Station (Market St at 5th St)	37.783899	-122.408445	56.0	Koshland Park	37.773414	-122.427317	4938	Subscriber	No
2060874	341	2019-10-15 08:57:37.9680	2019-10-15 09:03:19.4830	465.0	San Francisco Caltrain Station (King St at 4th...	37.776329	-122.394438	27.0	Beale St at Harrison St	37.788059	-122.391865	9949	Subscriber	No
1385457	321	2019-07-06 18:26:17.5420	2019-07-06 18:31:38.9430	98.0	Valencia St at 16th St	37.765052	-122.421866	356.0	Valencia St at Clinton Park	37.769188	-122.422285	2267	Subscriber	No
216450	123	2019-02-25 18:27:00.2850	2019-02-25 18:29:03.4220	223.0	16th St Mission BART Station 2	37.764765	-122.420091	110.0	17th & Folsom Street Park (17th St at Folsom St)	37.763708	-122.415204	4902	Subscriber	No

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	bike_share_for_all_trip
2124919	194	2019-10-07 08:08:17.8070	2019-10-07 08:11:32.6420	30.0	San Francisco Caltrain (Townsend St at 4th St)	37.776598	-122.395282	90.0	Townsend St at 7th St	37.771058	-122.402717	9585	Subscriber	No
1575356	581	2019-08-21 16:16:39.7660	2019-08-21 16:26:20.9590	80.0	Townsend St at 5th St	37.775235	-122.397437	77.0	11th St at Natoma St	37.773507	-122.416040	9940	Subscriber	Yes
2297074	347	2019-11-08 19:53:40.9170	2019-11-08 19:59:27.9410	179.0	Telegraph Ave at 27th St	37.816073	-122.267886	7.0	Frank H Ogawa Plaza	37.804562	-122.271738	9724	Subscriber	No
1565152	990	2019-08-22 18:16:31.3900	2019-08-22 18:33:01.8120	13.0	Commercial St at Montgomery St	37.794231	-122.402923	97.0	14th St at Mission St	37.768265	-122.420110	9665	Subscriber	No
1066631	372	2019-06-28 09:00:58.3610	2019-06-28 09:07:10.8670	99.0	Folsom St at 15th St	37.767037	-122.415442	109.0	17th St at Valencia St	37.763316	-122.421904	1134	Subscriber	No

Ford Go Bike Communication¶

Preliminary Wrangling¶

Loading the dataset¶

Cleaning¶

Dataset structure¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Univariate Exploration¶

Distribution of Rental duration in minutes¶

Distribution of Customer vs Subscribers¶

Day, Month and Hour vs Rentals¶

Distance travelled by Subscribers and Customers¶

Percentage of Rental duration for Subscribers and Customers¶

Bivariate Exploration¶

Distribution of rental duration in minutes (subscribers)¶

Distribution of rental duration in minutes (customers)¶

Distribution of rental duration in minutes (all users)¶

Distance travelled in miles by Subscribers¶

Duration in mins of Rentals for Subscribers and Customers¶

Stations with the most customers rentals¶

Stations with the most subscribers rentals¶

Multivariate Exploration¶

Distribution of Start Day and Start Hour for Customers¶

Heat map of Time vs Subscribers Rentals.¶

Locations¶

Top 40 locations of rentals for Customers¶

Top 40 locations of rentals for Subscribers¶

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

Were there any interesting or surprising interactions between features?¶

Summary¶

	duration_mins	start_day	end_day	start_month	end_month	start_hour	end_hour	duration_sec	start_time	end_time	...	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	bike_share_for_all_trip
0	1347.083333	Thu	Fri	Jan	Feb	17	16	80825	2019-01-31 17:57:44.613	2019-02-01 16:24:49.864	...	Foothill Blvd at 42nd Ave	37.775745	-122.213037	196.0	Grand Ave at Perkins St	37.808894	-122.256460	4861	Subscriber	No
1	1098.333333	Thu	Fri	Jan	Feb	20	15	65900	2019-01-31 20:58:33.886	2019-02-01 15:16:54.173	...	Cyril Magnin St at Ellis St	37.785881	-122.408915	134.0	Valencia St at 24th St	37.752428	-122.420628	5506	Subscriber	No
2	1043.883333	Thu	Fri	Jan	Feb	18	11	62633	2019-01-31 18:06:52.924	2019-02-01 11:30:46.530	...	Downtown Berkeley BART	37.870139	-122.268422	157.0	65th St at Hollis St	37.846784	-122.291376	2717	Customer	No
3	744.666667	Thu	Fri	Jan	Feb	19	8	44680	2019-01-31 19:46:09.719	2019-02-01 08:10:50.318	...	Church St at Duboce Ave	37.770083	-122.429156	53.0	Grove St at Divisadero	37.775946	-122.437777	4557	Customer	No
4	1011.816667	Thu	Fri	Jan	Feb	14	7	60709	2019-01-31 14:19:01.541	2019-02-01 07:10:51.065	...	Steuart St at Market St	37.794130	-122.394430	28.0	The Embarcadero at Bryant St	37.787168	-122.388098	2100	Customer	No

	duration_mins	start_day	end_day	start_month	end_month	start_hour	end_hour	duration_sec	start_time	end_time	...	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	bike_share_for_all_trip	distance_miles
810167	4.050000	Sun	Sun	Apr	Apr	15	15	243	2019-04-07 15:17:24.810	2019-04-07 15:21:28.021	...	37.783521	-122.431158	285.0	Webster St at O'Farrell St	37.783521	-122.431158	6084	Customer	No	0.000000
921433	13.283333	Thu	Thu	May	May	8	8	797	2019-05-23 08:42:29.948	2019-05-23 08:55:46.981	...	37.794130	-122.394430	93.0	4th St at Mission Bay Blvd S	37.770407	-122.391198	1661	Subscriber	No	1.648546
326430	37.733333	Fri	Fri	Feb	Feb	17	17	2264	2019-02-08 17:03:15.836	2019-02-08 17:41:00.795	...	37.802746	-122.413579	139.0	Garfield Square (25th St at Harrison St)	37.751017	-122.411901	4972	Subscriber	No	3.575307
2184996	4.766667	Mon	Mon	Nov	Nov	17	17	286	2019-11-25 17:19:36.241	2019-11-25 17:24:23.108	...	37.811377	-122.265192	197.0	El Embarcadero at Grand Ave	37.808848	-122.249680	11724	Customer	No	0.864625
2109201	7.283333	Tue	Tue	Oct	Oct	18	19	437	2019-10-08 18:53:08.739	2019-10-08 19:00:26.414	...	37.778742	-122.392741	22.0	Howard St at Beale St	37.789756	-122.394643	11667	Subscriber	No	0.768077

	duration_mins	start_day	end_day	start_month	end_month	start_hour	end_hour	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	bike_share_for_all_trip	distance_miles
1362333	5	Wed	Wed	Jul	Jul	17	17	81.0	Berry St at 4th St	37.775880	-122.393170	28.0	The Embarcadero at Bryant St	37.787168	-122.388098	2446	Subscriber	No	0.827648
2297706	21	Fri	Fri	Nov	Nov	18	18	16.0	Steuart St at Market St	37.794130	-122.394430	345.0	Hubbell St at 16th St	37.766483	-122.398279	12075	Subscriber	No	1.921775
101906	9	Fri	Fri	Jan	Jan	10	11	6.0	The Embarcadero at Sansome St	37.804770	-122.403234	15.0	San Francisco Ferry Building (Harry Bridges Pl...	37.795392	-122.394203	38	Customer	No	0.814211
1333149	20	Mon	Mon	Jul	Jul	18	18	126.0	Esprit Park	37.761634	-122.390648	133.0	Valencia St at 22nd St	37.755213	-122.420975	3753	Subscriber	No	1.715033
2046552	9	Wed	Wed	Oct	Oct	19	19	183.0	Telegraph Ave at 19th St	37.808702	-122.269927	197.0	El Embarcadero at Grand Ave	37.808848	-122.249680	1376	Subscriber	No	1.105299

Ford Go Bike Communication¶

Preliminary Wrangling¶

Loading the dataset¶

Cleaning¶

Dataset structure¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Univariate Exploration¶

Distribution of Rental duration in minutes¶

Distribution of Customer vs Subscribers¶

Day, Month and Hour vs Rentals¶

How many enrolled in bike sharing scheme ?¶

Distance travelled by Subscribers and Customers¶

Percentage of Rental duration for Subscribers and Customers¶

Bivariate Exploration¶

Distribution of rental duration in minutes (subscribers)¶

Distribution of rental duration in minutes (customers)¶

Distribution of rental duration in minutes (all users)¶

Distance travelled in miles by Subscribers¶

Duration in mins of Rentals for Subscribers and Customers¶

Stations with the most customers rentals¶

Stations with the most subscribers rentals¶

Stations with the most bike share rentals¶

Multivariate Exploration¶

Distribution of Start Day and Start Hour for Customers¶

Heat map of Time vs Subscribers Rentals.¶

Locations¶

Top 40 locations of rentals for Customers¶

Top 40 locations of rentals for Subscribers¶

Top 40 locations of rentals for Bike Share for all users¶

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

Were there any interesting or surprising interactions between features?¶

Summary¶